2 + 2 
## [1] 4
nhanes_small
## # A tibble: 10,000 x 14
##      age sex    height weight   bmi diabetes diabetes_age phys_active_days
##    <int> <fct>   <dbl>  <dbl> <dbl> <fct>           <int>            <int>
##  1    34 male     165.   87.4  32.2 No                 NA               NA
##  2    34 male     165.   87.4  32.2 No                 NA               NA
##  3    34 male     165.   87.4  32.2 No                 NA               NA
##  4     4 male     105.   17    15.3 No                 NA               NA
##  5    49 female   168.   86.7  30.6 No                 NA               NA
##  6     9 male     133.   29.8  16.8 No                 NA               NA
##  7     8 male     131.   35.2  20.6 No                 NA               NA
##  8    45 female   167.   75.7  27.2 No                 NA                5
##  9    45 female   167.   75.7  27.2 No                 NA                5
## 10    45 female   167.   75.7  27.2 No                 NA                5
## # ... with 9,990 more rows, and 6 more variables: phys_active <fct>,
## #   tot_chol <dbl>, bp_sys_ave <int>, bp_dia_ave <int>, smoke_now <fct>,
## #   poverty <dbl>
nhanes_small %>%
    filter(!is.na(diabetes)) %>%
    group_by(diabetes, sex) %>%
    summarise(mean_age = mean(age, na.rm = TRUE),
              mean_bmi = mean(bmi, na.rm = TRUE)) %>%
    ungroup() %>% 
    knitr::kable(caption = "Table 1. Mean Age and BMI.")
## `summarise()` has grouped output by 'diabetes'. You can override using the `.groups` argument.
Table 1. Mean Age and BMI.
diabetes sex mean_age mean_bmi
No female 36.46581 26.21885
No male 34.34953 26.10141
Yes female 59.90476 33.70212
Yes male 58.64764 31.53878

Image by Dimitri Houtteman from Pixabay.

knitr::include_graphics(here::here("c:/Users/au322271/Desktop/LearningR/doc/images/kitten.jpg"))
Kitten attacking flowers!

Kitten attacking flowers!